Anomaly Detection in Intrusion Detection Systems

Abstract

Intrusion detection systems (IDS) play a critical role in network security by monitoring systems and network traffic to detect anomalies and attacks. This study explores the different types of IDS, including host-based and network-based, along with their deployment scenarios. A key focus is on incorporating anomaly detection techniques within IDS to identify novel and unknown threats that evade signature-based methods. Statistical approaches like outlier detection and machine learning techniques like neural networks are discussed for building effective anomaly detection models. Data collection and preprocessing techniques, including feature engineering, are examined. Both unsupervised techniques like clustering and density estimation and supervised methods like classification are covered. Evaluation datasets and performance metrics for assessing anomaly detection models are highlighted. Challenges like curse of dimensionality and concept drift are outlined. Emerging trends include integrating deep learning and explainable AI into anomaly detection. Overall, this comprehensive study examines the role of anomaly detection within IDS, delves into various techniques and algorithms, surveys evaluation practices, discusses limitations and challenges, and provides insights into future research directions to advance network security through improved anomaly detection capabilities.